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For subgroups $H,P\le G$, define $x\sim y$ when $y\in HxP$. The identity elements show reflexivity, inverses show symmetry, and multiplication shows transitivity. Thus the double cosets $HxP$ partition $G$.

Let $H$ act by left multiplication on the set of left cosets $G/P$. The orbit of $xP$ consists of the cosets contained in $HxP$, so its size is $|HxP|/|P|$. Its stabilizer isThe orbit-stabilizer theorem therefore gives

Suppose $P$ is a Sylow subgroup of $G$, with $|P|=p^a$, and write the largest power of $p$ dividing $|H|$ as $p^b$. If every $H\cap xP{x}^{-1}$ had order at most $p^{b-1}$, the displayed formula would make every double-coset size divisible by $p^{a+1}$. Their sum $|G|$ would then also be divisible by $p^{a+1}$, contradicting the choice of $P$. Hence some $H\cap xP{x}^{-1}$ has order $p^b$ and is a Sylow $p$-subgroup of $H$. This is the Sylow subgroup of a subgroup from double cosets argument.

To count the general linear group over a finite field $G{L}_n({\mathbb{F}}_p)$, choose its columns successively. There are $p^n-1$ choices for the first, $p^n-p$ for the second, and $p^n-p^k$ for column $k+1$. ConsequentlyNone of the factors $p^j-1$ is divisible by $p$, so the upper unitriangular group is a Sylow $p$-subgroup: it has one arbitrary field entry in each of the $n(n-1)/2$ positions above the diagonal and hence order $p^{n(n-1)/2}$. The permutation matrices form a subgroup isomorphic to the symmetric group $S_n$.

By Cayley theorem, every finite group $G$ embeds in $S_{|G|}$, and permutation matrices embed this symmetric group in $G{L}_{|G|}({\mathbb{F}}_p)$. The latter has the explicit Sylow $p$-subgroup just described, so the result proved in the first part, applied to the embedded copy of $G$, proves that every finite group has a Sylow $p$-subgroup.

The counting part of the Sylow theorems says that if $p^a$ is the largest power of $p$ dividing $|G|$, then the number $n_p$ of Sylow $p$-subgroups satisfies

Finally, let $|G|=pq$ with prime numbers $p>q$. The Sylow counts giveso $n_p=1$ and the Sylow $p$-subgroup is a normal subgroup. Also $n_q\mid p$ and $n_q\equiv 1(\mathrm{mod}q)$. If $n_q=1$, both Sylow subgroups are normal; their elements commute, so $G$ is their direct product and is abelian. In the nonabelian case one must therefore have $n_q=p$, whenceor equivalently $q\mid p-1$, as recorded by nonabelian group of order pq.

Because $A{x}^{\ast }=b$, the error $e^{(k)}=x^{\ast }-x^{(k)}$ obeysThus the heavy-ball error propagation matrix is

If $A=\mathrm{diag}({\lambda }_1,\dots ,{\lambda }_n)$, permute the coordinates fromtoFor the corresponding permutation matrix $P$,

The permutation matrix cycles the three coordinate vectors, so three applications give $C^3=I$. Consequently $C^{2}C=C{C}^2=I$, proving $C^{-1}=C^2$. Since $16=5\cdot 3+1$,

The permutation matrix acts by $A(x,y,z{)}^T=(y,z,x{)}^T$. Applying this cyclic permutation three times returns every vector to itself, proving $A^3=I$. Explicitly $A^2=\left(\begin{array}{ccc}0& 0& 1\\ 1& 0& 0\\ 0& 1& 0\end{array}\right)$ and multiplication once more gives the identity matrix.

Let ${\mathbf{e}}_1,{\mathbf{e}}_2,{\mathbf{e}}_3$ be the Cartesian basis vectors, with the given vector $\mathbf{x}={\mathbf{e}}_1$. The permutation matrix cycles them forward:Thus three applications return the original vector.

Denote the four listed matrices, in order, by $I,P,Q,W$. As permutation matrices, $P,Q,W$ act on the four basis vectors by $(12)(34)$, $(13)(24)$ and $(14)(23)$, respectively. Direct multiplication givesThese relations prove closure and show that each element is its own inverse. The identity is included and matrix multiplication is associative, so the matrices form a group.

The relations coincide with those of the Klein four-group $D_2$: take its half-turn generator to $P$, a reflection generator to $Q$, and their product to $W$. All four matrices are distinct, so this assignment is a faithful representation of $D_2$. It is the four-point regular representation of the Klein four-group.

Every matrix fixes $v=(1,1,1,1{)}^T$, since it permutes its coordinates. Thereforeis a one-dimensional invariant subspace, nonzero and strictly smaller than the four-dimensional space. This proves the representation is reducible. In fact the four mutually orthogonal sign vectors $(1,1,1,1)$, $(1,1,-1,-1)$, $(1,-1,1,-1)$ and $(1,-1,-1,1)$ give invariant lines and completely diagonalize the representation, as in the normalized Hadamard matrix construction.

Use the left regular representation, defined on the ordered basis $(e_I,e_R,\dots ,e_{R^5},e_{m_1},\dots ,e_{m_6})$ by $D_{\mathrm{reg}}(g)e_h=e_{gh}$. Reading the $m_4$ row of the original PDF table gives the permutationThus an explicit $12\times 12$ permutation matrix, in six-by-six blocks, isEach column has its one in the row labelled by the left product; this specifies the convention completely. Since $P^2=I_6$, the full matrix squares to $I_{12}$ and is its own inverse. Therefore for every integer, including negative integers,This is also immediate from $m_4^2=I$ and the multiplication law for a representation.

The two one-dimensional representations are the trivial representation, ${\rho }_{+}(g)=1$, and the sign representation, ${\rho }_{-}(g)=\mathrm{sgn}(g)$. The first respects multiplication immediately. For the second, the sign of a permutation is the determinant of its permutation matrix, and multiplicativity of the determinant proves ${\rho }_{-}(gh)={\rho }_{-}(g){\rho }_{-}(h)$. They are inequivalent since a transposition has images $1$ and $-1$, and conjugation by a nonzero scalar cannot change a one-dimensional matrix.

Their group homomorphism kernels areBoth are subgroups, indeed normal subgroups, because they are kernels. In the second case $A_4$ is the alternating group of even permutations, with twelve elements.

The characters are their scalar values, since the trace of a one-dimensional matrix is its only entry. In the class order from part (b),The character inner product is the group average of ${\chi }_{+}\overline{{\chi }_{-}}$, soEach has squared norm $(1+6+3+8+6)/24=1$, in agreement with character orthogonality for distinct irreducible representations.

Gaussian elimination uses elementary row operations to remove entries below successive pivot elements, reducing a linear system to one with an upper triangular matrix. Back substitution then solves that system. It also computes a reusable matrix factorization, so additional right-hand sides can be handled by forward substitution and back substitution without repeating the elimination.

For elimination without row exchanges, let $A^{(1)}=A$. At step $k$, provided the pivot element $a_{kk}^{(k)}$ is nonzero, form the elimination multipliersand perform ${\text{row}}_i\leftarrow {\text{row}}_i-m_{ik}{\text{row}}_k$, applying the same operation to the right-hand side. More explicitly the resulting entries satisfy $a_{ij}^{(k+1)}=a_{ij}^{(k)}-m_{ik}{a}_{kj}^{(k)}$ for $i>k$, $j\ge k$.

Let $E_k=I-{\sum }_{i>k}{m}_{ik}{e}_i{e}_k^T$, where $e_i$ are the standard basis vectors. ThenThe matrix $L$ is a unit lower triangular matrix storing the elimination multipliers, while $U$ is an upper triangular matrix:Solving $Ax=b$ becomes $Ly=b$ followed by $Ux=y$. If a required pivot element vanishes, an unpermuted LU decomposition can fail even for an invertible matrix. Row exchanges, typically selected by partial pivoting, instead produce $PA=LU$ with a permutation matrix $P$; then solve $Ly=Pb$, $Ux=y$.

Use Gaussian elimination with partial pivoting, choosing a largest absolute entry in the uneliminated part of each pivot column. The first pivot is two in row one, with multipliers $1/2,1/2,1/5$ for rows two, three and four. The second-column candidates are then $-1,0,-2/5$, so row two stays as pivot and the remaining nonzero multiplier is $2/5$ for row four. The augmented matrix at this stage isThe third pivot is three, so swap rows three and four. The final multiplier is $-1/4$, leaving last pivot $-2$. Carrying that swap through the previously stored multipliers givesThe transformed right side is $(3,1,0,0{)}^T$. Back substitution gives $z=0$, $y=0$, $x=-1$ and $w=1$, hence $\boxed{(w,x,y,z)=(1,-1,0,0)}$.

For the eigenvalue claim, put $S=P^{T}L$. It is invertible because a permutation matrix and a unit lower triangular matrix are invertible. The factorization gives $A=SU$, soSimilar matrices have the same characteristic polynomial and therefore the same eigenvalues with algebraic multiplicities. This proves the claim without changing the prescribed elimination method.

Take a singular value decomposition $F=Q_{1}D{Q}_2^T$, with $D=\mathrm{diag}({\lambda }_1,{\lambda }_2,{\lambda }_3)$ positive and $Q_1,Q_2\in SO(3)$. Such a choice is possible because $\det F>0$. Left material frame indifference removes $Q_1$, and right material isotropy removes $Q_2$, giving $W(F)=W(D)$.

Moreover $W(D)$ is unchanged when the positive diagonal entries are permuted. Every permutation can be implemented by a signed permutation matrix of determinant one; the signs disappear in conjugating a diagonal matrix. Thus the energy depends only on the unordered principal stretches.

The three invariants determine precisely this unordered triple: the roots of $t^3-I_B{t}^2+I{I}_{B}t-II{I}_B$ are ${\lambda }_1^2,{\lambda }_2^2,{\lambda }_3^2$, and their positive square roots give the stretches. Consequently, on the set of admissible invariant triples, define $h(I_B,I{I}_B,II{I}_B)$ to be $W(D)$. The preceding argument proves that this is well defined and thatNot every positive triple is realizable by three positive roots. Since the requested statement only asserts existence of a function on $(0,\infty {)}^3$, extend $h$ arbitrarily on the nonrealizable triples; no choice there affects the formula for a deformation in continuum mechanics. Smooth invariant representations, where needed for differentiating the constitutive equation, are understood locally on the realizable set.

Suppose an invertible matrix has two LU factorizations, $A=L_1{U}_1=L_2{U}_2$, with $L_1,L_2$ unit lower triangular matrices and $U_1,U_2$ upper triangular matrices. All four factors are invertible matrices, since their square products are invertible. Multiplication by the appropriate inverse matrices givesBy the supplied closure properties, the left side is a unit lower triangular matrix and the right side is an upper triangular matrix. A matrix of both types is diagonal; its diagonal entries are all one. Thus both sides are the identity matrix, whenceQualification of the printed wording: this establishes uniqueness if the specified LU factorization exists. Invertibility alone does not guarantee an unpivoted LU factorization. For example $\left(\begin{array}{cc}0& 1\\ 1& 0\end{array}\right)$ is invertible, but in a product $\left(\begin{array}{cc}1& 0\\ \ell & 1\end{array}\right)\left(\begin{array}{cc}{u}_{11}& u_{12}\\ 0& u_{22}\end{array}\right)$, the zero top-left entry forces $u_{11}=0$, making the bottom-left entry $\ell u_{11}=0$ as well, contrary to the required value one. A permutation matrix is needed in general, as in PLU factorization.

Write the determinant in its Leibniz formula for determinants, where $S_n$ is the set of permutations of $\{1,\dots ,n\}$:The permutation sign is justified by the given row-interchange property: a permutation matrix is obtained from the identity matrix by row interchanges, and every interchange changes its determinant by a factor $-1$. Reversing a sequence of interchanges constructs the inverse permutation with the same number of interchanges. Consequently $\mathrm{sgn}({\sigma }^{-1})=\mathrm{sgn}(\sigma )$. The Leibniz formula for determinants can itself be obtained by expanding the rows multilinearly in the standard basis: repeated selected coordinate rows contribute zero, and the remaining terms are precisely these signed permutation matrices.

Since $(A^T{)}_{ij}=a_{ji}$, the same formula givesReindex the product with $j=\sigma (i)$ and reindex the sum with $\tau ={\sigma }^{-1}$. Both reindexings are bijections, soThus $\det (A)=\det (A^T)$ for every real square matrix, including singular matrices. This proof does not assume the analogous column-operation rules in order to establish the transpose identity.

A triangular matrix has determinant equal to the product of its diagonal entries, so $\det L=1$ and $\det U=3\cdot 4\cdot (-11)=-132$. A permutation matrix exchanging two rows has determinant $-1$. Using multiplicativity of the determinant in $PA=LU$ yields $(-1)\det A=-132$. Hence $\det A=132$.

The permutation matrix isRows three and four of the identity matrix have been interchanged, so left multiplication interchanges those rows of any matrix.

The $j$th column of $AE$ is $A(E{e}_j)$. The permutation matrix fixes $e_1,e_2$ and interchanges $e_3,e_4$, so these columns are $A{e}_1,A{e}_2,A{e}_4,A{e}_3$. They are exactly the first, second, fourth and third columns of $A$. Thus right multiplication by $E$ interchanges columns three and four, leaving the others unchanged.

The first left multiplication swaps rows three and four. The final right multiplication swaps columns three and four, restoring the two diagonal ones to their diagonal positions while exchanging the earlier entries of the two rows. ThereforeThe two specified zeros at positions $(3,4)$ and $(4,3)$ are important. Conjugation by this permutation matrix preserves this restricted lower triangular pattern; it would not preserve lower triangularity for an arbitrary matrix with a nonzero $(4,3)$ entry.

Choose a fixed time increment $h=\Delta t>0$, let $t_n=nh$, and approximate $\mathbf{c}(t_n)$ by ${\mathbf{c}}_n$. The Backward Euler method evaluates the transport term at the new time:Rearrange to the linear systemAlthough $A$ is a singular matrix, $B$ is an invertible matrix for every $h>0$. In each row, its diagonal entry is $1+2h$ and the sum of the absolute values of its off-diagonal entries is $2h$; hence it is a strictly diagonally dominant matrix. Its Gershgorin discs have centre $1+2h$ and radius $2h$, excluding zero. In particular, the zero eigenvalue of $A$ becomes an eigenvalue $1$ of $B$.

The algorithm uses Reusing LU factors for multiple right-hand sides:

Both triangular solves use the factors of $I-hA$. The LU factorization of the singular transport matrix $A$ from part (a) is not a solver for this new linear system. No matrix inverse needs to be formed. Since $A$ and $h$ are fixed, the same LU factorization serves every step; only the right-hand side changes. A varying $\mathbf{b}(t)$ would give $r_n={\mathbf{c}}_n-h\mathbf{b}(t_{n+1})$ without changing these factors.

As a check, multiplying the update by $1^T$ givesexactly reproducing the inventory balance at the sampled times, apart from roundoff error. The Backward Euler method has temporal global error $O(h)$ for smooth solutions, so its A-stability does not remove the need to select $h$ for accuracy.

We fix the convention $PA=LU$: the permutation matrix $P$ acts on the rows of $A$, and the same row ordering must be applied to $b$ before the Triangular solves for LU decomposition. Equivalently, $A=P^{T}LU$, since $P^{-1}=P^T$. LAPACK documents its factors as $A=PLU$ instead, so check the defining equation rather than just the letter $P$.^[ref] Partial pivoting selects the largest absolute candidate in the current column, among rows $k,\dots ,n$, before forming elimination multipliers.^[ref]

A zero first pivot element does not imply a singular matrix. For example,Unpivoted Gaussian elimination would divide $2$ by zero. Indeed, no $A_0=LU$ with a unit lower triangular matrix $L$ exists: $u_{11}=0$ would force $(LU{)}_{21}=l_{21}{u}_{11}=0$, contradicting $(A_0{)}_{21}=2$. One row interchange resolves the obstruction. For $b_0=(1,5{)}^T$, use $P{b}_0=(5,1{)}^T$; back substitution then gives $x=(1,1{)}^T$. Using the original $b_0$ with these factors would solve a different linear system.

Partial pivoting bounds each elimination multiplier by one in absolute value in exact arithmetic. This helps control roundoff error, but intermediate entries can still grow; it is not an unconditional guarantee of numerical stability for every matrix.^[ref] It also does not remove the original problem's condition number. For the diagonal matrix $D=\mathrm{diag}(1,\epsilon )$ with $0<\epsilon <1$, the condition number in the Euclidean norm is ${\kappa }_2(D)=1/\epsilon$. Row permutations preserve this condition number, because they preserve Euclidean norms. Even an exact solve changes from $(1,1{)}^T$ to $(1,1+\eta /\epsilon {)}^T$ when the right-hand side changes from $(1,\epsilon {)}^T$ to $(1,\epsilon +\eta {)}^T$. This sensitivity is a property of the linear system, not a zero-pivot element failure.^[ref]

A matrix factorization incorporating a permutation matrix, a unit lower triangular matrix and an upper triangular matrix. The convention $A=PLU$ differs from $PA=LU$ by replacing $P$ with its inverse. When pivoting during elimination, previously stored lower-triangular multipliers must be permuted consistently with the rows.